University of Chicago
Pritzker School of Molecular Engineering
Stem Cell Engineering
Dr. Joyce Chen is an Assistant Professor in the Pritzker School of Molecular Engineering and the Ben May Department for Cancer Research at the University of Chicago. She earned her PhD in Biomedical Engineering from Cornell University and a PharmD in Pharmaceutical Sciences from Zhejiang University.
Before the Tumor Forms
Most cancers are found only after their origin is already gone. At the University of Chicago, Professor Joyce Chen and her lab are using matched stem cell-derived immune and cancer cells, engineered cellular sensors, and lab-grown organoids to rebuild the earliest moments of disease from scratch, and why studying a tumor after it already formed was never going to reveal how it began.
By BioBuilt Editorial
By the time a cancer is discovered, its origin story is already lost. A tumor large enough to show up on a scan may already contain billions of cells and dozens of accumulated genetic changes, and untangling which of those changes happened first, in which specific cell, in what order, is close to impossible after the fact. Cancer research has largely had to make do with that limitation, studying disease from the outside in: sequence the tumor that's already there, infer what must have happened earlier, and hope the inference holds up.
Huanhuan Joyce Chen, an assistant professor at the University of Chicago's Pritzker School of Molecular Engineering and the Ben May Department for Cancer Research, has built her lab around a different strategy entirely. Rather than studying cancer backward from an already-formed tumor, start with normal human cells, built from scratch under controlled conditions, and watch disease begin in something close to real time. What's interesting about this approach is that it doesn't try to get better at reading the evidence left behind by cancer. It tries to remove the need to read evidence at all, by engineering a version of the crime scene you can watch happen from the very first second.
Matching the Players
Tumors don't grow in isolation. They're surrounded by immune cells, and among the most important are macrophages, cells capable of either attacking a tumor or, confusingly, helping it grow and evade the immune system depending on their state. In some tumors, these tumor-associated macrophages can make up a substantial share of the entire mass, and shifting them from a tumor-supporting state toward a tumor-attacking one has become one of the more promising directions in cancer immunotherapy. But that promise depends on first understanding what actually causes a macrophage to switch sides, and studying that relationship has traditionally meant combining a cancer cell line from one patient with immune cells from an unrelated donor.
That standard setup carries a serious flaw, in Chen's account. "The immune cells may recognize the cancer cells as genetically foreign, making it difficult to distinguish a true tumor–immune interaction from a response caused by genetic mismatch," she explained. It's a subtle point, but it changes what a researcher can actually conclude from an experiment like this. If a mismatched macrophage starts behaving aggressively, there's no clean way to know whether it's reacting to the cancer or simply to a stranger's cells, the biological equivalent of a control group that was never really controlled. Chen's lab sidesteps the problem by generating macrophages and cancer cells as matched pairs from the same human pluripotent stem cell source. "This allows us to study how tumor cells and immune cells interact without the additional variability caused by differences between donors," she said.
With a shared genetic background held constant, her lab can introduce a single defined mutation into just the cancer cell population and ask a precise question: does this specific alteration, on its own, cause tumor cells to push nearby macrophages toward a tumor-supporting state? "These questions are difficult to address precisely in standard cell lines or animal models," Chen noted, "because of their genetic complexity and species-specific differences." The engineering contribution here isn't that matched stem cell lines reveal some entirely new biological phenomenon. It's that they let her attribute an observed effect to the right cause, which, in a field this crowded with confounding variables, may be just as valuable.
A Biological Logic Circuit
Macrophages don't flip between a simple "attacking cancer" and "helping cancer" switch. "Macrophages do not normally switch between two completely separate 'on' and 'off' states," Chen said. "Their behavior exists along a spectrum and may change gradually as the tumor develops," which makes the cells notoriously difficult to monitor with a conventional experiment that only checks in once, at a single endpoint, long after whatever mattered has already happened.
Chen's lab has engineered a cellular sensor to track that shift as it happens instead, and the design borrows an idea straight out of electrical engineering rather than biology. "An effective sensor must measure the activity of biological pathways rather than depend on a single marker," she said. In practice, that means identifying transcription factors and signaling pathways that become more active as macrophages drift toward tumor-promoting or tumor-suppressing behavior, then engineering pathway-responsive DNA elements connected to a fluorescent or luminescent reporter. "As the pathway becomes more active, the reporter signal becomes stronger, allowing us to follow the transition over time," Chen explained. To keep the sensor from firing on ordinary inflammation or cellular stress, her lab integrates readouts from several genes or pathways at once, "essentially creating a biological logic circuit."
That phrase is worth sitting with, because it's really the crux of the engineering idea. A single marker is a fragile thing to build a diagnosis around, since plenty of unrelated biology can make one gene switch on. Combine several independent signals and require all of them to cross a threshold together, though, and you get something closer to an AND gate: a system that's far less likely to fire on noise, precisely because noise rarely lines up across several channels at once. It's the same logic behind a spam filter that flags an email only when multiple suspicious signals show up together, applied here to a living cell instead of a message. As Chen put it, "the long-term goal is to observe macrophage-state changes dynamically in living cells rather than examining only a single endpoint after the cells have been collected."
Timing Is Everything
Perhaps the most counterintuitive finding to come out of Chen's research is that a cancer-driving mutation doesn't have a fixed effect. Most public discussion of cancer genetics treats a mutation almost like a diagnosis in itself, as though finding a particular altered gene tells you what disease you're looking at. Chen's work complicates that picture directly: the same mutation, she's found, can behave completely differently depending on exactly when, developmentally speaking, it happens to strike.
"A stem or progenitor cell has a relatively flexible gene-regulatory program and may still have the potential to generate several cell types," Chen explained. "The same mutation introduced into a mature, specialized cell may have a very different effect because many developmental pathways have already been shut down." The outcome isn't uniform even within that framework. "In some cases, a mutation may cause a progenitor cell to expand abnormally," she said. "In other cases, a mature cell may need to undergo partial de-differentiation before it can form a tumor." Her lab has demonstrated the underlying principle directly rather than just theorizing about it: in one line of research, the group showed that depleting the tumor-suppressor genes TP53 and RB1 while overexpressing the MYC oncogene in pulmonary neuroendocrine cells, derived from human embryonic stem cells at a specific developmental stage, was sufficient to produce malignant, metastatic small cell lung cancer. The same genetic alterations, introduced at a different developmental moment or in a different cell type, might not produce cancer at all, or might produce an entirely different disease altogether.
That's part of why the same mutation can turn up across many different cancer types while producing tumors that look and behave nothing alike, a fact that's genuinely strange the first time you encounter it, and that a lot of popular science writing glosses over in favor of the simpler, cleaner story. "The cell of origin, its developmental stage, the surrounding tissue environment, and the order in which mutations occur all contribute to the final tumor identity," Chen said. Her stem cell models let her introduce an identical mutation at multiple points along a developmental timeline. "We can compare what happens when an alteration occurs in an early lung progenitor, a differentiated epithelial cell, or a pulmonary neuroendocrine cell," she said, a level of precise control over exactly when a mutation strikes that isn't really available in a patient's own tissue, where you only ever get to see the end result, never the moment of origin.
A Body in a Dish
Between a flat layer of cells in a petri dish and a full living organ, there's a middle ground that's become one of the most important tools in modern biology: the organoid, a stem cell-derived structure that contains multiple interacting cell types arranged in something closer to real tissue architecture. "Traditional cell lines are usually grown as a flat layer containing only one cell type," Chen said. "They are experimentally convenient, but they do not reproduce the three-dimensional structure, cellular diversity, or developmental organization of human tissue." Her lab's organoids are built specifically to close that gap: "stem cell-derived organoids can contain multiple interacting cell types arranged in a more tissue-like architecture."
What makes this middle ground genuinely useful, rather than just a compromise between two imperfect options, is that some biological interactions seem to depend on exactly the kind of tissue-level complexity a flat cell culture can't offer, but don't require a whole animal either. Chen's team has put human lung organoid models to work on questions well beyond cancer for exactly this reason. In one line of research, her group has used them to study how the fungus Aspergillus fumigatus interacts with lung tissue, including whether the infection induces DNA damage and mutational patterns associated with cancer, a human-specific interaction she notes "may not be reproduced accurately in a mouse." That's a meaningful qualifier. Mouse lungs and human lungs don't always respond to the same infections the same way, so a finding that only shows up in human tissue would be invisible to an animal study entirely, no matter how carefully it was run.
She's careful not to overstate the case against animal models, though, and it's worth noting that this measured tone runs through most of her answers. "Animal models remain essential because they capture circulation, immunity, metabolism, and whole-organ physiology," she said. "However, species differences can sometimes make it difficult to translate findings directly to humans." In her view, the two approaches aren't competitors: "organoids complement animal studies by allowing us to test mechanisms directly in organized human tissue."
From Pharmacy to Engineering to Cancer Biology
Chen's own path into this kind of research wasn't a straight line through biology. She earned a PharmD in pharmaceutical sciences at Zhejiang University in China before moving to Cornell University for a PhD in biomedical engineering, where she trained under Michael Shuler, a pioneer of engineered "body on a chip" systems that model how drugs move through interconnected human tissues. She then completed a postdoctoral fellowship with Harold Varmus at Weill Cornell Medicine, a Nobel laureate who helped establish that cancer-causing genes originate from mutations in the body's own normal genes, rather than being introduced entirely from outside.
It's an unusual sequence, pharmaceutical training, then engineering, then cancer biology, and it's tempting to read it as three separate careers rather than one continuous one. But there's a thread running through all three: a pharmacist thinks about how a substance moves through and acts on the body, an engineer thinks about how to build a system that behaves predictably, and a cancer biologist thinks about what happens when a biological system stops behaving the way it's supposed to. Chen's research sits at the point where all three questions overlap. She's candid, though, that combining them isn't effortless. "Each discipline has its own language, experimental standards, and way of thinking," she said. "A stem cell biologist may focus on cell identity and differentiation, an engineer may focus on system design and reproducibility, and a cancer biologist may focus on mutations, tumor growth, and therapeutic response. Integrating these perspectives requires substantial collaboration and constant learning." There's a technical tension buried in that combination, too. "A model must be complex enough to reproduce meaningful human biology but controlled enough to determine why a particular result occurs," she said. "Increasing biological complexity can sometimes reduce experimental reproducibility, so we must carefully balance realism with precision."
What she gets in return, she says, is the ability to ask questions no single field could answer alone. "Stem cell biology gives us the ability to generate human cell types," she explained. "Developmental biology tells us how those cells normally acquire their identities. Tissue engineering allows us to organize them into functional systems. Cancer biology helps us understand how mutations and environmental factors disrupt those systems. Together, these approaches allow us not only to observe disease but also to reconstruct how it begins." It's a pattern worth noticing across biomedical engineering more broadly, and not just in Chen's lab: some of the most useful research seems to come from people who trained in one discipline long enough to think like it, then moved somewhere else before they could be fully boxed in by it.
Catching Cancer Before It Exists
Most cancers aren't caught until years after the first cell first went wrong. "By the time a tumor becomes clinically detectable, it may contain millions or billions of cells and many genetic and epigenetic alterations," Chen said. "It is therefore extremely difficult to reconstruct which event happened first." Her stem cell-based models offer a way around that limitation entirely: start with normal human cells, introduce one defined genetic change at a time, and watch, directly, what happens next. "We can observe how the first mutation affects cell identity, tissue organization, communication with neighboring cells, and responses to environmental exposures," she said. "We can also test whether the timing and order of mutations determine whether a cell remains normal, becomes precancerous, or develops into an aggressive tumor."
Chen believes this approach could eventually reveal disease states that no patient study could ever catch, simply because they don't last long enough, or announce themselves clearly enough, to be caught in a person rather than a dish. "These systems may reveal transient precancerous states that exist only briefly and are therefore almost impossible to capture in patients," she said. "They may also help us identify the earliest biomarkers released by abnormal cells and determine when a developing cancer is still reversible or vulnerable to prevention." Her hope for where this leads is direct: "I hope stem cell-based models will move cancer research beyond studying only established tumors. They may allow us to study, and eventually intercept, the transition from normal development to disease before a tumor fully forms."
It's a striking reframe of what cancer research is even trying to do. Most of the tools people associate with fighting cancer, chemotherapy, radiation, immunotherapy, are built to treat a tumor that already exists. Chen's lab is working toward something that comes earlier in the timeline entirely, a version of oncology aimed at a moment when there's no tumor to treat yet at all, only a single cell, quietly beginning to go somewhere it shouldn't, and, if her models work the way she hopes, a moment when it might still be possible to talk it out of going there.